Algorithmovigilance, lessons from pharmacovigilance

被引：3
作者：
Balendran, Alan [1 ,2 ]
Benchoufi, Mehdi [1 ,2 ]
Evgeniou, Theodoros [3 ]
Ravaud, Philippe [1 ,2 ,4 ,5 ]
机构：
[1] Univ Paris Cite, Paris, France
[2] Univ Sorbonne Paris Nord, Ctr Res Epidemiol & Stat CRESS, Inserm, INRAE, Paris, France
[3] INSEAD, Fontainebleau, France
[4] Hop Hotel Dieu, Ctr Epidemiol Clin, AP HP, Paris, France
[5] Columbia Univ, Dept Epidemiol, Mailman Sch Publ Hlth, New York, NY USA
来源：
NPJ DIGITAL MEDICINE | 2024年 / 7卷 / 01期
关键词：
CAUSALITY ASSESSMENT; HEALTH;
D O I：
10.1038/s41746-024-01237-y
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
Artificial Intelligence (AI) systems are increasingly being deployed across various high-risk applications, especially in healthcare. Despite significant attention to evaluating these systems, post-deployment incidents are not uncommon, and effective mitigation strategies remain challenging. Drug safety has a well-established history of assessing, monitoring, understanding, and preventing adverse effects in real-world usage, known as pharmacovigilance. Drawing inspiration from pharmacovigilance methods, we discuss concepts that can be adapted for monitoring AI systems in healthcare. This discussion aims to improve responses to adverse effects and potential incidents and risks associated with AI deployment in healthcare but also beyond.
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收藏
页数：6
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